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分享运筹学最好的教科书、软件Lingo14及其学生版License

斯坦褔大学FREDERICK S. HILLIER和GERALD J. LIEBERMAN所著的运筹学教科书，是至今运筹学领域最好的教学巨著，最新为第9版：
INTRODUCTION TO OPERATIONS RESEARCH 9th Ed by F.S.HILLIER & G.J. LIEBERMAN (2010)
(电子书原版本，非复印版，文字清楚)
请转如下网址下载：https://bbs.pinggu.org/thread-2920457-1-1.html

本书很多学习例子都结合indo/Lingo软件应用和示例。
希望本书和上述软件对学习运筹学的朋友有帮助！

indo/Lingo是学习运筹学最好的软件。
Lingo v14学生版License和Lingo v14软件下载：https://bbs.pinggu.org/thread-2920457-1-1.html

INTRODUCTION TO OPERATIONS RESEARCH 9th Ed by F.S.HILLIER & G.J. LIEBERMAN (2010)内容如下：
FREDERICK S. HILLIER
Stanford University
GERALD J. LIEBERMAN
Late of Stanford University

TABLE OF CONTENTS
PREFACE xviii
CHAPTER 1
Introduction 1
1.1 The Origins of Operations Research 1
1.2 The Nature of Operations Research 2
1.3 The Impact of Operations Research 3
1.4 Algorithms and OR Courseware 5
Selected References 7
Problems 7
CHAPTER 2
Overview of the Operations Research Modeling Approach 8
2.1 Defining the Problem and Gathering Data 8
2.2 Formulating a Mathematical Model 11
2.3 Deriving Solutions from the Model 13
2.4 Testing the Model 16
2.5 Preparing to Apply the Model 17
2.6 Implementation 18
2.7 Conclusions 19
Selected References 19
Problems 20
CHAPTER 3
Introduction to Linear Programming 23
3.1 Prototype Example 24
3.2 The Linear Programming Model 30
3.3 Assumptions of Linear Programming 36
3.4 Additional Examples 42
3.5 Formulating and Solving Linear Programming Models on a Spreadsheet 60
3.6 Formulating Very Large Linear Programming Models 68
3.7 Conclusions 75
Selected References 75
Learning Aids for This Chapter on Our Website 76
Problems 77
Case 3.1 Auto Assembly 86
Previews of Added Cases on Our Website 88
Case 3.2 Cutting Cafeteria Costs 88
Case 3.3 Staffing a Call Center 88
Case 3.4 Promoting a Breakfast Cereal 88
vii
viii CONTENTS
CHAPTER 4
Solving Linear Programming Problems: The Simplex Method 89
4.1 The Essence of the Simplex Method 89
4.2 Setting Up the Simplex Method 94
4.3 The Algebra of the Simplex Method 97
4.4 The Simplex Method in Tabular Form 103
4.5 Tie Breaking in the Simplex Method 108
4.6 Adapting to Other Model Forms 111
4.7 Postoptimality Analysis 129
4.8 Computer Implementation 137
4.9 The Interior-Point Approach to Solving Linear Programming Problems 140
4.10 Conclusions 145
Appendix 4.1 An Introduction to Using LINDO and LINGO 145
Selected References 149
Learning Aids for This Chapter on Our Website 149
Problems 150
Case 4.1 Fabrics and Fall Fashions 158
Previews of Added Cases on Our Website 160
Case 4.2 New Frontiers 160
Case 4.3 Assigning Students to Schools 160
CHAPTER 5
The Theory of the Simplex Method 161
5.1 Foundations of the Simplex Method 161
5.2 The Simplex Method in Matrix Form 172
5.3 A Fundamental Insight 181
5.4 The Revised Simplex Method 184
5.5 Conclusions 187
Selected References 187
Learning Aids for This Chapter on Our Website 188
Problems 188
CHAPTER 6
Duality Theory and Sensitivity Analysis 195
6.1 The Essence of Duality Theory 196
6.2 Economic Interpretation of Duality 203
6.3 Primal–Dual Relationships 206
6.4 Adapting to Other Primal Forms 211
6.5 The Role of Duality Theory in Sensitivity Analysis 215
6.6 The Essence of Sensitivity Analysis 217
6.7 Applying Sensitivity Analysis 225
6.8 Performing Sensitivity Analysis on a Spreadsheet 245
6.9 Conclusions 259
Selected References 260
Learning Aids for This Chapter on Our Website 260
Problems 261
Case 6.1 Controlling Air Pollution 274
Previews of Added Cases on Our Website 275
Case 6.2 Farm Management 275
Case 6.3 Assigning Students to Schools, Revisited 275
Case 6.4 Writing a Nontechnical Memo 275
CHAPTER 7
Other Algorithms for Linear Programming 276
7.1 The Dual Simplex Method 276
7.2 Parametric Linear Programming 280
7.3 The Upper Bound Technique 285
7.4 An Interior-Point Algorithm 287
7.5 Conclusions 298
Selected References 299
Learning Aids for This Chapter on Our Website 299
Problems 300
CHAPTER 8
The Transportation and Assignment Problems 304
8.1 The Transportation Problem 305
8.2 A Streamlined Simplex Method for the Transportation Problem 319
8.3 The Assignment Problem 334
8.4 A Special Algorithm for the Assignment Problem 342
8.5 Conclusions 346
Selected References 347
Learning Aids for This Chapter on Our Website 347
Problems 348
Case 8.1 Shipping Wood to Market 356
Previews of Added Cases on Our Website 357
Case 8.2 Continuation of the Texago Case Study 357
Case 8.3 Project Pickings 357
CHAPTER 9
Network Optimization Models 358
9.1 Prototype Example 359
9.2 The Terminology of Networks 360
9.3 The Shortest-Path Problem 363
9.4 The Minimum Spanning Tree Problem 368
9.5 The Maximum Flow Problem 373
9.6 The Minimum Cost Flow Problem 380
9.7 The Network Simplex Method 389
9.8 A Network Model for Optimizing a Project’s Time-Cost Trade-Off 399
9.9 Conclusions 410
Selected References 411
Learning Aids for This Chapter on Our Website 411
Problems 412
Case 9.1 Money in Motion 420
Previews of Added Cases on Our Website 423
Case 9.2 Aiding Allies 423
Case 9.3 Steps to Success 423
CHAPTER 10
Dynamic Programming 424
10.1 A Prototype Example for Dynamic Programming 424
10.2 Characteristics of Dynamic Programming Problems 429
10.3 Deterministic Dynamic Programming 431
CONTENTS ix
10.4 Probabilistic Dynamic Programming 451
10.5 Conclusions 457
Selected References 457
Learning Aids for This Chapter on Our Website 457
Problems 458
CHAPTER 11
Integer Programming 464
11.1 Prototype Example 465
11.2 Some BIP Applications 468
11.3 Innovative Uses of Binary Variables in Model Formulation 473
11.4 Some Formulation Examples 479
11.5 Some Perspectives on Solving Integer Programming Problems 487
11.6 The Branch-and-Bound Technique and Its Application to Binary
Integer Programming 491
11.7 A Branch-and-Bound Algorithm for Mixed Integer
Programming 503
11.8 The Branch-and-Cut Approach to Solving BIP Problems 509
11.9 The Incorporation of Constraint Programming 515
11.10 Conclusions 521
Selected References 522
Learning Aids for This Chapter on Our Website 523
Problems 524
Case 11.1 Capacity Concerns 533
Previews of Added Cases on Our Website 535
Case 11.2 Assigning Art 535
Case 11.3 Stocking Sets 535
Case 11.4 Assigning Students to Schools, Revisited Again 536
CHAPTER 12
Nonlinear Programming 537
12.1 Sample Applications 538
12.2 Graphical Illustration of Nonlinear Programming Problems 542
12.3 Types of Nonlinear Programming Problems 546
12.4 One-Variable Unconstrained Optimization 552
12.5 Multivariable Unconstrained Optimization 557
12.6 The Karush-Kuhn-Tucker (KKT) Conditions for Constrained Optimization 563
12.7 Quadratic Programming 567
12.8 Separable Programming 573
12.9 Convex Programming 580
12.10 Nonconvex Programming (with Spreadsheets) 588
12.11 Conclusions 592
Selected References 593
Learning Aids for This Chapter on Our Website 593
Problems 594
Case 12.1 Savvy Stock Selection 605
Previews of Added Cases on Our Website 606
Case 12.2 International Investments 606
Case 12.3 Promoting a Breakfast Cereal, Revisited 606
x CONTENTS
CHAPTER 13
Metaheuristics 607
13.1 The Nature of Metaheuristics 608
13.2 Tabu Search 615
13.3 Simulated Annealing 626
13.4 Genetic Algorithms 635
13.5 Conclusions 645
Selected References 646
Learning Aids for This Chapter on Our Website 646
Problems 647
CHAPTER 14
Game Theory 651
14.1 The Formulation of Two-Person, Zero-Sum Games 651
14.2 Solving Simple Games—A Prototype Example 653
14.3 Games with Mixed Strategies 658
14.4 Graphical Solution Procedure 660
14.5 Solving by Linear Programming 662
14.6 Extensions 666
14.7 Conclusions 667
Selected References 667
Learning Aids for This Chapter on Our Website 667
Problems 668
CHAPTER 15
Decision Analysis 672
15.1 A Prototype Example 673
15.2 Decision Making without Experimentation 674
15.3 Decision Making with Experimentation 680
15.4 Decision Trees 686
15.5 Using Spreadsheets to Perform Sensitivity Analysis on Decision Trees 690
15.6 Utility Theory 700
15.7 The Practical Application of Decision Analysis 707
15.8 Conclusions 708
Selected References 709
Learning Aids for This Chapter on Our Website 709
Problems 710
Case 15.1 Brainy Business 720
Preview of Added Cases on Our Website 722
Case 15.2 Smart Steering Support 722
Case 15.3 Who Wants to be a Millionaire? 722
Case 15.4 University Toys and the Engineering Professor Action Figures 722
CHAPTER 16
Markov Chains 723
16.1 Stochastic Processes 723
16.2 Markov Chains 725
16.3 Chapman-Kolmogorov Equations 732
CONTENTS xi
16.4 Classification of States of a Markov Chain 735
16.5 Long-Run Properties of Markov Chains 737
16.6 First Passage Times 743
16.7 Absorbing States 745
16.8 Continuous Time Markov Chains 748
Selected References 753
Learning Aids for This Chapter on Our Website 753
Problems 754
CHAPTER 17
Queueing Theory 759
17.1 Prototype Example 760
17.2 Basic Structure of Queueing Models 760
17.3 Examples of Real Queueing Systems 765
17.4 The Role of the Exponential Distribution 767
17.5 The Birth-and-Death Process 773
17.6 Queueing Models Based on the Birth-and-Death Process 777
17.7 Queueing Models Involving Nonexponential Distributions 790
17.8 Priority-Discipline Queueing Models 798
17.9 Queueing Networks 803
17.10 The Application of Queueing Theory 807
17.11 Conclusions 812
Selected References 812
Learning Aids for This Chapter on Our Website 813
Problems 814
Case 17.1 Reducing In-Process Inventory 826
Preview of an Added Case on Our Website 827
Case 17.2 Queueing Quandary 827
CHAPTER 18
Inventory Theory 828
18.1 Examples 829
18.2 Components of Inventory Models 831
18.3 Deterministic Continuous-Review Models 833
18.4 A Deterministic Periodic-Review Model 843
18.5 Deterministic Multiechelon Inventory Models for Supply
Chain Management 848
18.6 A Stochastic Continuous-Review Model 866
18.7 A Stochastic Single-Period Model for Perishable Products 870
18.8 Revenue Management 882
18.9 Conclusions 890
Selected References 890
Learning Aids for This Chapter on Our Website 891
Problems 892
Case 18.1 Brushing Up on Inventory Control 902
Previews of Added Cases on Our Website 904
Case 18.2 TNT: Tackling Newsboy’s Teachings 904
Case 18.3 Jettisoning Surplus Stock 904
xii CONTENTS
CHAPTER 19
Markov Decision Processes 905
19.1 A Prototype Example 905
19.2 A Model for Markov Decision Processes 908
19.3 Linear Programming and Optimal Policies 911
19.4 Policy Improvement Algorithm for Finding Optimal Policies 915
19.5 Discounted Cost Criterion 920
19.6 Conclusions 928
Selected References 928
Learning Aids for This Chapter on Our Website 929
Problems 929
CHAPTER 20
Simulation 934
20.1 The Essence of Simulation 934
20.2 Some Common Types of Applications of Simulation 946
20.3 Generation of Random Numbers 951
20.4 Generation of Random Observations from a Probability Distribution 955
20.5 Outline of a Major Simulation Study 959
20.6 Performing Simulations on Spreadsheets 963
20.7 Conclusions 979
Selected References 981
Learning Aids for This Chapter on Our Website 982
Problems 983
Case 20.1 Reducing In-Process Inventory, Revisited 989
Case 20.2 Action Adventures 989
Previews of Added Cases on Our Website 990
Case 20.3 Planning Planers 990
Case 20.4 Pricing under Pressure 990
APPENDIXES
1. Documentation for the OR Courseware 991
2. Convexity 993
3. Classical Optimization Methods 998
4. Matrices and Matrix Operations 1001
5. Table for a Normal Distribution 1006
PARTIAL ANSWERS TO SELECTED PROBLEMS 1008
INDEXES
Author Index 1023
Subject Index 1029

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